The OdinSchool Blog

From tips to break into the industry to actionable suggestions on how to make a career change, explore our blogs to read all things Data Science.

Debunking Data Science Myths: 5 Common Misconceptions to Unlearn

Debunking Data Science Myths: 5 Common Misconceptions to Unlearn

So you have set out to build your career in Data Science. But you are bombarded with a profusion of blogs, tutorials, and videos to choose from while doing your research. It’s easy for a beginner to get lost in the sea of uncurated information about Data Science. So where do you start?

The most trending field in the tech sector is unfortunately rife with myths. The many misconceptions floating around about the field make it challenging especially for people trying to get their basics right and break into the field.

Here are 5 myths about Data Science you need to unlearn:

1. AI will Substitute Data Science Professionals

This is one of the most popular misconceptions that build reservations in the minds of aspiring professionals. AI can certainly automate some of the tasks performed by these professionals. For instance, AI can detect relevant prediction features, build basic models, and produce hundreds of variations of models. But AI will not understand what a particular set of data means to an organization or its business. Data science will still require human judgment to turn data into valuable insights.

Truth - AI is not a threat to aspiring data science professionals. They will rather turn out to be very effective resources that can facilitate complex data simulations. Besides, data science professionals are closely involved in the process of building AI.

2. Data Science is Only Meant for Mathematicians and Statisticians

Data Science is a combination of Mathematics, Statistics, Data Modeling, Computer Science and Programming, Visualization, and several other technologies. Yes, professionals with foundational knowledge in statistics and mathematics will have an edge in the field. But if they cannot utilize the formulae to build accurate models, their knowledge is of no value in the domain. A good data science professional needs to bring more than just statistics and mathematics to the table. He or she also needs to have good data intuition and sound business acumen among other core data science skills.

Truth - A good statistician or mathematician does not necessarily become a good data scientist by default. Professionals from a diverse range of backgrounds can join this field.

3. Data Science is Just Complex Programming

Data science is all about collecting relevant data, understanding and leveraging it to form critical business decisions and strategies. The mission of a data science professional is to wrangle and analyze data using a wide range of techniques and tools. Programming is just one of those techniques employed by them. Having strong programming skills will certainly come in handy. But there is more to this domain than complex coding.

Truth - Data Science is about building opportunities, identifying problems, and making data-informed decisions. Coding or programming is only one part of the whole process.

4. Any Kind of Technical Experience is Sufficient to Break into Data Science

Your previous experience and domain knowledge will help you comprehend business problems, ask the right questions, and provide quality solutions. However, in order to excel as a data science professional, you need to get your hands dirty and work with real data using the right kind of tools and algorithms. One credible way to acquire this expertise is by joining data science bootcamps; they prioritize practical learning over theoretical knowledge.

Truth - Although your previous experience definitely adds value, you still need to acquire proper hands-on experience in relevant technologies to make a career in data science.

5. A Full-Time Degree is a Must to Become A Data Scientist

Of course, formal education is important and is a valuable credential in your resume. But most organizations are no longer rigid about candidates having formal degrees in data science. Regardless of your academic or professional background, credible certification programs and intensive bootcamps that offer a project-based, hands-on learning experience are effective ways of launching a career in data science.

Truth - Formal conventional education in data science is not necessary to become a professional in this field of work. Learning to program, analyze, model, and wrangle data is definitely possible with the right guidance and hands-on learning.

While choosing a career as a data scientist is a brilliant idea, it's important to get your hands on verified information. It's vital that you have an in-depth understanding of the field before beginning, so take some time to validate your knowledge; your understanding of the field will influence your important career decisions.

Join OdinSchool to acquire credible hands-on experience in Data Science.

5 Ways to Gain Hands-on Experience in Data Science

5 Ways to Gain Hands-on Experience in Data Science

Are you all set to take the leap into the dynamic world of data science? How do you get a competitive edge in a field that already has thousands of aspiring data science practitioners?

Being book-smart is not enough to break into a field as competitive as data science. The key is to get hands-on experience so you can become a viable candidate.

A chief factor that carves your identity in the market, your hands-on experience can help you understand your strengths, skills, and most importantly, enrich your experience in real-world settings. Besides, hands-on experience is a necessity to fully comprehend the professional challenges in data science.

Here are 5 ways for you to gain hands-on experience in data science:

1. Personal Projects
Working on personal projects is a good way to start. They need not necessarily be complete end-to-end projects, but the idea is to focus on perfecting a specific skill. You can also showcase your projects on platforms such as GitHub.
Here are some small projects you can work on:

  • Collecting and cleaning data sets - This type of project will expose you to different data formats, data collection methods, and data cleaning tasks.
  • Making predictions - Work on logistic regression and linear regression models to learn predictions.
  • Exploratory Data Analysis (EDA) - EDA is one of the best types of projects to exhibit your skills in framing and investigating the right questions, using different plots, and visualizing your inferences.

2. Hackathons
Hackathon participants are required to build projects that have real-world applications. These events demand the conversion of hypotheses and theories into actions, thus helping participants put their theoretical skills to the test. Hackathons simulate real working environments and deadlines, thereby giving contestants a peek into the real settings of a data science job. They also stimulate business thinking; apart from creating data models, contestants need to leverage their critical thinking skills to understand what data strategies would benefit a business.

3. Tutorials
By creating tutorials, you tell your potential employers that you have what it takes to tackle the role of a data science professional. Tutorials can help you demonstrate your in-demand data science skills as well as communication skills. For instance, you can clean a dataset while you walk your audience through the steps. Or you could create a detailed blog post where you explain the relevance of a certain in-demand skill and how one can execute it.

4. Open-Source Projects
Participating in open source projects is one of the best ways to enrich your coding and technical skills. You can also become familiar with data science libraries, version control, and git by contributing to such projects. These projects also further your communication skills as you would be required to stay constantly in touch with other data science professionals involved in the project. In short, you get to create codes that benefit other projects and also expand your professional network.

5. Data Science Bootcamps
Data science bootcamps prioritize practical skills over theories. Here, learners receive job-oriented training wherein they are exposed to all the challenges of a data science job. With the optimal usage of individual and group exercises, projects, simulations, and discussions, candidates are more aligned to industry demands. Bootcamp curriculum is industry-vetted; learners are given hands-on training in all in-demand data science skills.

Until you gather experience with data science tools, real-world data sets, and develop a sound data intuition, you cannot make your way into data science. In short, building your career in data science is never about degrees but about how well you can wield your practical skills. This is why acquiring ample hands-on experience is inevitable to your data science career.

Join OdinSchool’s Data Science Bootcamp to Gain Hands-on Experience
Optimize Your Resume for Your Data Science Career With These Tips

Optimize Your Resume for Your Data Science Career With These Tips

A Data Science employer sifts through hundreds of resumes every day. What can you do to stand out?

The key mantra is ‘customize your resume to suit the requirements of every company you apply to’.

Framing your expertise in a way that ties into the existing problems or goals of the industry is the best way to land a lucrative role. Not only should your resume make you stand out in the crowd, but it should also project your potential as a solution your prospective employer is looking for.

Here are 5 tips for optimizing your resume for landing data scientist jobs.

1. Spotlight Your In-Demand Skills

Gleaning critical business insights from raw data takes more than just storing and managing skills. In-demand skills are what make a data science candidate desirable. While building your data science resume, it is important to draw the attention of your employer to the same.

Points to remember:

  • Make sure your in-demand skills come first which are then followed by your least impressive skills.
  • You can use platforms such as Upwork to distinguish each of your skills and highlight them aesthetically to draw attention.
  • Mention your skill level (beginner, intermediate, expert) for each entry in the list for the reference of your hiring manager.

2. Present Your Relevant Achievements

The ultimate objective of a resume is to highlight your strengths. This is why it is recommended to present all your achievements in the top half of your resume.

Points to remember:

  • Mention only relevant achievements - Do not talk about accomplishments that are not associated with Data Science.
  • Be quantifiable wherever possible - By giving a quantifiable understanding, you describe the impact you created better. Here, you can talk about the important targets you achieved.
  • Do not make the section wordy - Your resume should ideally fit in a single page. Describe your accomplishments in a single sentence.

3. Use Keywords from the Job Description

If you need your resume to rank well for the position you are seeking, use the right keywords. Tailor it to the job you are applying to so that your profile will match the requirements. The more keywords you can add, the better.

Points to remember:

  • Do not omit plural words, spelling variations, abbreviations, or numbers.
  • Do not stuff your resume with the keywords; use them contextually and sensibly.

4. Do not List Your Soft Skills, Explain Them

In order to demonstrate how good your soft skills are, explain them on your resume. For instance, if you are talking about communication skills, describe how you used this skill to succeed in your previous job. Tangible points like ‘managed client correspondence and stakeholder communication’, ‘took care of a team of 10+ members’, and so on will explain your potential better.

Points to remember:

  • Explain your skills in one sentence.
  • Add skills that you think will suit the culture of the organization that you are applying to.
  • Do not forget to include relevant soft skills that sell such as storytelling, business acumen, and adaptability.

5. In Case of Career Switch, Go for Skill-Based Resume Templates

If you are breaking into Data Science from a completely unrelated field, your best choice is a skill-based resume. In this scenario, your work history holds no significance, making a chronological depiction of your work experience irrelevant. Your best bet, in this case, is a data science skills resume that will emphasize relevant skills.

Points to remember:

  • Have an extensive objective statement that focuses on the needs of the employer. Target the specific position and add the right keywords.
  • Frame a narrative about yourself where you do not disregard your professional history, but rather use it to your advantage.
With so many candidates vying for data science roles, it can be tricky to stand apart from your peers and demonstrate your expertise. But your resume is an important tool to draw the attention of hiring managers. It can also be leveraged to prove what you are worth when it comes to specific projects and skills.

Join OdinSchool to acquire in-demand Data Science skills.